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Phylliade/ikpy | scripts/hand_follow.py | follow_hand | def follow_hand(poppy, delta):
"""Tell the right hand to follow the left hand"""
right_arm_position = poppy.l_arm_chain.end_effector + delta
poppy.r_arm_chain.goto(right_arm_position, 0.5, wait=True) | python | def follow_hand(poppy, delta):
"""Tell the right hand to follow the left hand"""
right_arm_position = poppy.l_arm_chain.end_effector + delta
poppy.r_arm_chain.goto(right_arm_position, 0.5, wait=True) | [
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Phylliade/ikpy | src/ikpy/inverse_kinematics.py | inverse_kinematic_optimization | def inverse_kinematic_optimization(chain, target_frame, starting_nodes_angles, regularization_parameter=None, max_iter=None):
"""
Computes the inverse kinematic on the specified target with an optimization method
Parameters
----------
chain: ikpy.chain.Chain
The chain used for the Inverse k... | python | def inverse_kinematic_optimization(chain, target_frame, starting_nodes_angles, regularization_parameter=None, max_iter=None):
"""
Computes the inverse kinematic on the specified target with an optimization method
Parameters
----------
chain: ikpy.chain.Chain
The chain used for the Inverse k... | [
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The chain used for the Inverse kinematics.
target_frame: numpy.array
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Phylliade/ikpy | src/ikpy/chain.py | Chain.forward_kinematics | def forward_kinematics(self, joints, full_kinematics=False):
"""Returns the transformation matrix of the forward kinematics
Parameters
----------
joints: list
The list of the positions of each joint. Note : Inactive joints must be in the list.
full_kinematics: bool
... | python | def forward_kinematics(self, joints, full_kinematics=False):
"""Returns the transformation matrix of the forward kinematics
Parameters
----------
joints: list
The list of the positions of each joint. Note : Inactive joints must be in the list.
full_kinematics: bool
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Phylliade/ikpy | src/ikpy/chain.py | Chain.inverse_kinematics | def inverse_kinematics(self, target, initial_position=None, **kwargs):
"""Computes the inverse kinematic on the specified target
Parameters
----------
target: numpy.array
The frame target of the inverse kinematic, in meters. It must be 4x4 transformation matrix
initi... | python | def inverse_kinematics(self, target, initial_position=None, **kwargs):
"""Computes the inverse kinematic on the specified target
Parameters
----------
target: numpy.array
The frame target of the inverse kinematic, in meters. It must be 4x4 transformation matrix
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Phylliade/ikpy | src/ikpy/chain.py | Chain.plot | def plot(self, joints, ax, target=None, show=False):
"""Plots the Chain using Matplotlib
Parameters
----------
joints: list
The list of the positions of each joint
ax: matplotlib.axes.Axes
A matplotlib axes
target: numpy.array
An optio... | python | def plot(self, joints, ax, target=None, show=False):
"""Plots the Chain using Matplotlib
Parameters
----------
joints: list
The list of the positions of each joint
ax: matplotlib.axes.Axes
A matplotlib axes
target: numpy.array
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Phylliade/ikpy | src/ikpy/chain.py | Chain.from_urdf_file | def from_urdf_file(cls, urdf_file, base_elements=None, last_link_vector=None, base_element_type="link", active_links_mask=None, name="chain"):
"""Creates a chain from an URDF file
Parameters
----------
urdf_file: str
The path of the URDF file
base_elements: list of s... | python | def from_urdf_file(cls, urdf_file, base_elements=None, last_link_vector=None, base_element_type="link", active_links_mask=None, name="chain"):
"""Creates a chain from an URDF file
Parameters
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urdf_file: str
The path of the URDF file
base_elements: list of s... | [
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blockchain-certificates/cert-issuer | cert_issuer/signer.py | check_internet_off | def check_internet_off(secrets_file_path):
"""If internet off and USB plugged in, returns true. Else, continues to wait..."""
while True:
if internet_on() is False and os.path.exists(secrets_file_path):
break
else:
print("Turn off your internet and plug in your USB to con... | python | def check_internet_off(secrets_file_path):
"""If internet off and USB plugged in, returns true. Else, continues to wait..."""
while True:
if internet_on() is False and os.path.exists(secrets_file_path):
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blockchain-certificates/cert-issuer | cert_issuer/signer.py | check_internet_on | def check_internet_on(secrets_file_path):
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while True:
if internet_on() is True and not os.path.exists(secrets_file_path):
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else:
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"""If internet on and USB unplugged, returns true. Else, continues to wait..."""
while True:
if internet_on() is True and not os.path.exists(secrets_file_path):
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blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/bitcoin/signer.py | verify_signature | def verify_signature(uid, signed_cert_file_name, issuing_address):
"""
Verify the certificate signature matches the expected. Double-check the uid field in the certificate and use
VerifyMessage to confirm that the signature in the certificate matches the issuing_address.
Raises error is verification fa... | python | def verify_signature(uid, signed_cert_file_name, issuing_address):
"""
Verify the certificate signature matches the expected. Double-check the uid field in the certificate and use
VerifyMessage to confirm that the signature in the certificate matches the issuing_address.
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blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/ethereum/connectors.py | EtherscanBroadcaster.get_balance | def get_balance(self, address, api_token):
"""
returns the balance in wei
with some inspiration from PyWallet
"""
broadcast_url = self.base_url + '?module=account&action=balance'
broadcast_url += '&address=%s' % address
broadcast_url += '&tag=latest'
if ap... | python | def get_balance(self, address, api_token):
"""
returns the balance in wei
with some inspiration from PyWallet
"""
broadcast_url = self.base_url + '?module=account&action=balance'
broadcast_url += '&address=%s' % address
broadcast_url += '&tag=latest'
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blockchain-certificates/cert-issuer | cert_issuer/blockchain_handlers/ethereum/connectors.py | EtherscanBroadcaster.get_address_nonce | def get_address_nonce(self, address, api_token):
"""
Looks up the address nonce of this address
Neccesary for the transaction creation
"""
broadcast_url = self.base_url + '?module=proxy&action=eth_getTransactionCount'
broadcast_url += '&address=%s' % address
broad... | python | def get_address_nonce(self, address, api_token):
"""
Looks up the address nonce of this address
Neccesary for the transaction creation
"""
broadcast_url = self.base_url + '?module=proxy&action=eth_getTransactionCount'
broadcast_url += '&address=%s' % address
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tensorflow/mesh | mesh_tensorflow/tpu_variables.py | ReplicatedVariable._dense_var_to_tensor | def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
"""Converts a variable to a tensor."""
# pylint: disable=protected-access
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return self._primary_var._dense_var_to_tensor(dtype, name, ... | python | def _dense_var_to_tensor(self, dtype=None, name=None, as_ref=False):
"""Converts a variable to a tensor."""
# pylint: disable=protected-access
if _enclosing_tpu_context() is None:
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tensorflow/mesh | mesh_tensorflow/auto_mtf/memory_estimator.py | MemoryEstimator._compute_layout_validator | def _compute_layout_validator(self):
"""Computes self._layout_validator."""
self._layout_validator = valid_layouts.LayoutValidator(self.mtf_graph,
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tensorflow/mesh | mesh_tensorflow/auto_mtf/memory_estimator.py | MemoryEstimator._compute_graph_interface | def _compute_graph_interface(self):
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tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | make_layer_stack | def make_layer_stack(layers=gin.REQUIRED, num_layers=6):
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Args:
layers: a list of subclasses of TransformerLayer
num_layers: an integer
Returns:
a LayerStack
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"""Configurable layer stack.
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num_layers: an integer
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tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | make_bitransformer | def make_bitransformer(
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
layout=None,
mesh_shape=None):
"""Gin-configurable bitransformer constructor.
In your config file you need to set the encoder and decoder layers like this:
encoder/make_layer_stack.layers = [
@transformer_l... | python | def make_bitransformer(
input_vocab_size=gin.REQUIRED,
output_vocab_size=gin.REQUIRED,
layout=None,
mesh_shape=None):
"""Gin-configurable bitransformer constructor.
In your config file you need to set the encoder and decoder layers like this:
encoder/make_layer_stack.layers = [
@transformer_l... | [
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tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.get_states | def get_states(self, n):
"""Get the next n recurrent states.
Called by layers in "incremental" mode.
Args:
n: an integer
Returns:
a list of n Tensors
"""
return self.states[len(self.new_states):len(self.new_states) + n] | python | def get_states(self, n):
"""Get the next n recurrent states.
Called by layers in "incremental" mode.
Args:
n: an integer
Returns:
a list of n Tensors
"""
return self.states[len(self.new_states):len(self.new_states) + n] | [
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tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.get_constant_state | def get_constant_state(self):
"""Read state that was written in "first_part" mode.
Returns:
a structure
"""
ret = self.constant_states[self.next_constant_state]
self.next_constant_state += 1
return ret | python | def get_constant_state(self):
"""Read state that was written in "first_part" mode.
Returns:
a structure
"""
ret = self.constant_states[self.next_constant_state]
self.next_constant_state += 1
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tensorflow/mesh | mesh_tensorflow/transformer/transformer.py | Context.nonpadding | def nonpadding(self):
"""Tensor with zeros in padding positions and ones elsewhere."""
if self.sequence_id is None:
return None
if self.sequence_id == 1:
return 1
else:
return mtf.cast(
mtf.not_equal(self.sequence_id, 0), self.activation_dtype) | python | def nonpadding(self):
"""Tensor with zeros in padding positions and ones elsewhere."""
if self.sequence_id is None:
return None
if self.sequence_id == 1:
return 1
else:
return mtf.cast(
mtf.not_equal(self.sequence_id, 0), self.activation_dtype) | [
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tensorflow/mesh | mesh_tensorflow/transformer/metrics.py | sequence_accuracy | def sequence_accuracy(labels, outputs):
"""Compute the sequence-level accuracy.
A sequence is only considered correct if all of its entries were predicted
correctly.
Args:
labels: ground-truth labels, shape=(batch, packed_seq_length)
outputs: predicted tokens, shape=(batch, seq_length)
Returns:
... | python | def sequence_accuracy(labels, outputs):
"""Compute the sequence-level accuracy.
A sequence is only considered correct if all of its entries were predicted
correctly.
Args:
labels: ground-truth labels, shape=(batch, packed_seq_length)
outputs: predicted tokens, shape=(batch, seq_length)
Returns:
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_input_names | def get_operation_input_names(self, operation_name):
"""Generates the names of all input tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an input tensor.
"""
for input_tensor in self._name_to_operation(operat... | python | def get_operation_input_names(self, operation_name):
"""Generates the names of all input tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an input tensor.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_output_names | def get_operation_output_names(self, operation_name):
"""Generates the names of all output tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an output tensor.
"""
for output_tensor in self._name_to_operation(op... | python | def get_operation_output_names(self, operation_name):
"""Generates the names of all output tensors of an operation.
Args:
operation_name: a string, the name of an operation in the graph.
Yields:
a string, the name of an output tensor.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_shape | def get_tensor_shape(self, tensor_name):
"""The tf.TensorShape of a tensor.
Args:
tensor_name: string, the name of a tensor in the graph.
Returns:
a tf.TensorShape
"""
tensor = self._name_to_tensor(tensor_name)
if isinstance(tensor, mtf.Tensor):
return tf.TensorShape(tensor.... | python | def get_tensor_shape(self, tensor_name):
"""The tf.TensorShape of a tensor.
Args:
tensor_name: string, the name of a tensor in the graph.
Returns:
a tf.TensorShape
"""
tensor = self._name_to_tensor(tensor_name)
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_num_entries | def get_tensor_num_entries(self, tensor_name, partial_layout=None,
mesh_dimension_to_size=None):
"""The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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"""The number of entries in a tensor.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_size | def get_tensor_size(self, tensor_name, partial_layout=None,
mesh_dimension_to_size=None):
"""The size of a tensor in bytes.
If partial_layout is specified, then mesh_dimension_to_size must also be. In
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Args:
tensor_name: a ... | python | def get_tensor_size(self, tensor_name, partial_layout=None,
mesh_dimension_to_size=None):
"""The size of a tensor in bytes.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_device | def get_tensor_device(self, tensor_name):
"""The device of a tensor.
Note that only tf tensors have device assignments.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a string or None, representing the device name.
"""
tensor = self._name_to_tensor(tensor_nam... | python | def get_tensor_device(self, tensor_name):
"""The device of a tensor.
Note that only tf tensors have device assignments.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a string or None, representing the device name.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_device | def get_operation_device(self, operation_name):
"""The device of an operation.
Note that only tf operations have device assignments.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a string or None, representing the device name.
"""
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"""The device of an operation.
Note that only tf operations have device assignments.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a string or None, representing the device name.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_tensor_mtf_dimension_names | def get_tensor_mtf_dimension_names(self, tensor_name):
"""The Mesh TensorFlow dimensions associated with a tensor.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a [string], the names of Mesh TensorFlow dimensions.
"""
tensor = self._name_to_tensor(tensor_name)
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"""The Mesh TensorFlow dimensions associated with a tensor.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a [string], the names of Mesh TensorFlow dimensions.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.get_operation_mtf_dimension_names | def get_operation_mtf_dimension_names(self, operation_name):
"""The Mesh TensorFlow dimensions associated with an operation.
Args:
operation_name: a string, name of an operation in the graph.
Returns:
a set(string), the names of Mesh TensorFlow dimensions.
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mtf_dimension_names = set... | python | def get_operation_mtf_dimension_names(self, operation_name):
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operation_name: a string, name of an operation in the graph.
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a set(string), the names of Mesh TensorFlow dimensions.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.set_tensor_final | def set_tensor_final(self, tensor_name):
"""Denotes a tensor as a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
"""
tensor = self._name_to_tensor(tensor_name)
self._final_tensors.add(tensor) | python | def set_tensor_final(self, tensor_name):
"""Denotes a tensor as a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
"""
tensor = self._name_to_tensor(tensor_name)
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.is_tensor_final | def is_tensor_final(self, tensor_name):
"""Whether a tensor is a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a boolean indicating whether the tensor was a final output.
"""
tensor = self._name_to_tensor(tensor_name)
return t... | python | def is_tensor_final(self, tensor_name):
"""Whether a tensor is a final output of the computation.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a boolean indicating whether the tensor was a final output.
"""
tensor = self._name_to_tensor(tensor_name)
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.compute_cost_graph | def compute_cost_graph(self, devices=None):
"""Computes a CostGraphDef protobuf based on this graph.
Defined in tensorflow/core/framework/cost_graph.proto.
Args:
devices: optional [string], the names of devices to consider. If
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface.compute_memory_contents_under_schedule | def compute_memory_contents_under_schedule(self, schedule):
"""The in-memory tensors present when executing each operation in schedule.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_operations | def _initialize_operations(self):
"""Initializer for _operations.
Raises:
TypeError: _graph is not a tf.Graph or mtf.Graph.
Returns:
a list of (tf.Operation or mtf.Operation)
"""
if isinstance(self._graph, tf.Graph):
return self._graph.get_operations()
elif isinstance(self._g... | python | def _initialize_operations(self):
"""Initializer for _operations.
Raises:
TypeError: _graph is not a tf.Graph or mtf.Graph.
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a list of (tf.Operation or mtf.Operation)
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_operation_name_to_id | def _initialize_operation_name_to_id(self):
"""Initializer for _operation_name_to_id.
Returns:
a {string: int}, mapping operation names to their index in _operations.
"""
operation_name_to_id = {}
for i, operation in enumerate(self._operations):
operation_name_to_id[operation.name] = i
... | python | def _initialize_operation_name_to_id(self):
"""Initializer for _operation_name_to_id.
Returns:
a {string: int}, mapping operation names to their index in _operations.
"""
operation_name_to_id = {}
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._initialize_tensor_name_to_ids | def _initialize_tensor_name_to_ids(self):
"""Initializer for _tensor_name_to_ids.
Returns:
a {string: (int, int)}, mapping the name of tensor T to the index of T's
operation in _operations and T's index in T's operation's outputs.
"""
tensor_name_to_ids = {}
for i, operation in enum... | python | def _initialize_tensor_name_to_ids(self):
"""Initializer for _tensor_name_to_ids.
Returns:
a {string: (int, int)}, mapping the name of tensor T to the index of T's
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._name_to_tensor | def _name_to_tensor(self, tensor_name):
"""The tensor with the given name.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a tf.Tensor or mtf.Tensor
"""
id1, id2 = self._tensor_name_to_ids[tensor_name]
return self._operations[id1].outputs[id2] | python | def _name_to_tensor(self, tensor_name):
"""The tensor with the given name.
Args:
tensor_name: a string, name of a tensor in the graph.
Returns:
a tf.Tensor or mtf.Tensor
"""
id1, id2 = self._tensor_name_to_ids[tensor_name]
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tensorflow/mesh | mesh_tensorflow/auto_mtf/graph_interface.py | GraphInterface._compute_initial_out_degree | def _compute_initial_out_degree(self):
"""The number of operations which use each tensor as input.
Returns:
a {string, int} mapping tensor name to the number of operations which use
it as input, or one plus that quantity if the tensor is final.
"""
out_degree = collections.defaultdict(int)
... | python | def _compute_initial_out_degree(self):
"""The number of operations which use each tensor as input.
Returns:
a {string, int} mapping tensor name to the number of operations which use
it as input, or one plus that quantity if the tensor is final.
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tensorflow/mesh | mesh_tensorflow/layers.py | layer_norm | def layer_norm(x, dim, epsilon=1e-6, name="layer_prepostprocess"):
"""Layer normalization over dimension dim.
Args:
x: a mtf.Tensor whose shape contains dim.
dim: a mtf.Dimension
epsilon: a floating point number
name: a string. variable scope.
Returns:
a mtf.Tensor with same shape as x.
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"""Layer normalization over dimension dim.
Args:
x: a mtf.Tensor whose shape contains dim.
dim: a mtf.Dimension
epsilon: a floating point number
name: a string. variable scope.
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a mtf.Tensor with same shape as x.
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tensorflow/mesh | mesh_tensorflow/layers.py | softmax_cross_entropy_with_logits | def softmax_cross_entropy_with_logits(logits, targets, vocab_dim, z_loss=0.0):
"""Per-example softmax loss.
if z_loss is nonzero, we add a loss equal to z_loss*log(z)^2, where z is the
partition function. Example value: z_loss=1e-4. Two uses of z_loss are:
- To keep the logits from drifting too far from zero... | python | def softmax_cross_entropy_with_logits(logits, targets, vocab_dim, z_loss=0.0):
"""Per-example softmax loss.
if z_loss is nonzero, we add a loss equal to z_loss*log(z)^2, where z is the
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tensorflow/mesh | mesh_tensorflow/layers.py | sigmoid_cross_entropy_with_logits | def sigmoid_cross_entropy_with_logits(logits, targets):
"""Sigmoid cross-entropy loss.
Args:
logits: a mtf.Tensor
targets: a mtf.Tensor with the same shape as logits
Returns:
a mtf.Tensor whose shape is equal to logits.shape
Raises:
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"""
if logits.sh... | python | def sigmoid_cross_entropy_with_logits(logits, targets):
"""Sigmoid cross-entropy loss.
Args:
logits: a mtf.Tensor
targets: a mtf.Tensor with the same shape as logits
Returns:
a mtf.Tensor whose shape is equal to logits.shape
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tensorflow/mesh | mesh_tensorflow/layers.py | dense_relu_dense | def dense_relu_dense(x,
hidden_channels,
dropout=0.0,
dropout_broadcast_dims=None,
master_dtype=tf.float32,
slice_dtype=tf.float32, name=None):
"""Hidden layer with ReLU activation followed by linear projection.
... | python | def dense_relu_dense(x,
hidden_channels,
dropout=0.0,
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master_dtype=tf.float32,
slice_dtype=tf.float32, name=None):
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tensorflow/mesh | mesh_tensorflow/layers.py | local_1d_halo_exchange | def local_1d_halo_exchange(k, v, num_w_blocks, w_dim, mask_right):
"""Halo exchange for keys and values for Local 1D attention."""
if num_w_blocks is not None:
if mask_right:
k = mtf.left_halo_exchange(k, num_w_blocks, w_dim, w_dim.size)
v = mtf.left_halo_exchange(v, num_w_blocks, w_dim, w_dim.size)... | python | def local_1d_halo_exchange(k, v, num_w_blocks, w_dim, mask_right):
"""Halo exchange for keys and values for Local 1D attention."""
if num_w_blocks is not None:
if mask_right:
k = mtf.left_halo_exchange(k, num_w_blocks, w_dim, w_dim.size)
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tensorflow/mesh | mesh_tensorflow/layers.py | local_2d_halo_exchange | def local_2d_halo_exchange(k, v, num_h_blocks, h_dim,
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"""Halo exchange for keys and values for Local 2D attention."""
for blocks_dim, block_size_dim, halo_size in [
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tensorflow/mesh | mesh_tensorflow/layers.py | local_2d_self_attention_spatial_blocks | def local_2d_self_attention_spatial_blocks(query_antecedent,
kv_channels,
heads,
memory_h_dim=None,
memory_w_dim=None,
... | python | def local_2d_self_attention_spatial_blocks(query_antecedent,
kv_channels,
heads,
memory_h_dim=None,
memory_w_dim=None,
... | [
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tensorflow/mesh | mesh_tensorflow/layers.py | multihead_attention_vars | def multihead_attention_vars(
mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, activation_dtype):
"""Deprecated version of multihead_attention_params with combine=True."""
return multihead_attention_params(
mesh, heads, io_channels, kv_channels,
mtf.VariableDType(master_dtype, s... | python | def multihead_attention_vars(
mesh, heads, io_channels, kv_channels,
master_dtype, slice_dtype, activation_dtype):
"""Deprecated version of multihead_attention_params with combine=True."""
return multihead_attention_params(
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tensorflow/mesh | mesh_tensorflow/layers.py | multihead_attention_params | def multihead_attention_params(mesh, heads, io_channels, kv_channels,
variable_dtype, combine=False):
"""Create Parameters for Multihead Attention.
If the combine flag is set to True, then we create only one variable
which stacks together all of the parameters. Otherwise, we creat... | python | def multihead_attention_params(mesh, heads, io_channels, kv_channels,
variable_dtype, combine=False):
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tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_ignore_padding | def attention_mask_ignore_padding(inputs, dtype=tf.float32):
"""Bias for encoder-decoder attention.
Args:
inputs: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
a mtf.Tensor with shape [..., memory_length_dim]
"""
inputs = rename_length_to_memory_length(inputs)
return mtf... | python | def attention_mask_ignore_padding(inputs, dtype=tf.float32):
"""Bias for encoder-decoder attention.
Args:
inputs: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
Returns:
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tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_autoregressive | def attention_mask_autoregressive(query_pos, dtype=tf.float32):
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Args:
query_pos: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
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a mtf.Tensor with shape [..., length_dim, memory_length_dim]
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"""Bias for self-attention where attention to the right is disallowed.
Args:
query_pos: a mtf.Tensor with shape [..., length_dim]
dtype: a tf.dtype
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a mtf.Tensor with shape [..., length_dim, memory_length_dim]
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tensorflow/mesh | mesh_tensorflow/layers.py | attention_mask_same_segment | def attention_mask_same_segment(
query_segment, memory_segment=None, dtype=tf.float32):
"""Bias for attention where attention between segments is disallowed.
Args:
query_segment: a mtf.Tensor with shape [..., length_dim]
memory_segment: a mtf.Tensor with shape [..., memory_length_dim]
dtype: a tf.d... | python | def attention_mask_same_segment(
query_segment, memory_segment=None, dtype=tf.float32):
"""Bias for attention where attention between segments is disallowed.
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query_segment: a mtf.Tensor with shape [..., length_dim]
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tensorflow/mesh | mesh_tensorflow/layers.py | multiplicative_jitter | def multiplicative_jitter(x, epsilon=1e-2):
"""Multiply values by a random number between 1-epsilon and 1+epsilon.
Makes models more resilient to rounding errors introduced by bfloat16.
This seems particularly important for logits.
Args:
x: a mtf.Tensor
epsilon: a floating point value
Returns:
... | python | def multiplicative_jitter(x, epsilon=1e-2):
"""Multiply values by a random number between 1-epsilon and 1+epsilon.
Makes models more resilient to rounding errors introduced by bfloat16.
This seems particularly important for logits.
Args:
x: a mtf.Tensor
epsilon: a floating point value
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tensorflow/mesh | mesh_tensorflow/layers.py | multihead_self_attention_memory_compressed | def multihead_self_attention_memory_compressed(x,
mask_right,
compression_factor,
kv_channels,
heads,
... | python | def multihead_self_attention_memory_compressed(x,
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tensorflow/mesh | mesh_tensorflow/layers.py | compress_mean | def compress_mean(x, dim, compression_factor):
"""Compress by taking group means.
Args:
x: a Tensor
dim: a dimension in x.shape
compression_factor: an integer
Returns:
a Tensor
"""
dims = x.shape.dims
pos = dims.index(dim)
compressed_dim = mtf.Dimension(dim.name, dim.size // compression_... | python | def compress_mean(x, dim, compression_factor):
"""Compress by taking group means.
Args:
x: a Tensor
dim: a dimension in x.shape
compression_factor: an integer
Returns:
a Tensor
"""
dims = x.shape.dims
pos = dims.index(dim)
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tensorflow/mesh | mesh_tensorflow/layers.py | embedding | def embedding(indices, vocab_dim, output_dim, variable_dtype, name="embedding"):
"""Embedding layer."""
weights = embedding_weights(
indices.mesh, vocab_dim, output_dim, variable_dtype, name)
return mtf.gather(weights, indices, vocab_dim) | python | def embedding(indices, vocab_dim, output_dim, variable_dtype, name="embedding"):
"""Embedding layer."""
weights = embedding_weights(
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tensorflow/mesh | mesh_tensorflow/transformer/transformer_layers.py | attention_params | def attention_params(context,
kv_dim,
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"""Attention Parameters for Transformer Layers.
The num_heads argument indicates the number of read-heads.
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tensorflow/mesh | mesh_tensorflow/transformer/metric_utils.py | get_metric_fns | def get_metric_fns(metric_names, labels, outputs):
"""Generate a dictionary of metric name to metric function.
Args:
metric_names: list of strings in the format "prefix/metric_function_name".
metric_function_name should refer to a function name in metrics.py. The
prefix will be included in the key ... | python | def get_metric_fns(metric_names, labels, outputs):
"""Generate a dictionary of metric name to metric function.
Args:
metric_names: list of strings in the format "prefix/metric_function_name".
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tensorflow/mesh | mesh_tensorflow/auto_mtf/scheduler.py | minimize_peak_memory | def minimize_peak_memory(graph, scheduler_alg):
"""Computes a schedule to minimize peak memory.
Args:
graph: an mtf.auto_mtf.graph_interface.GraphInterface.
scheduler_alg: a string, one of 'NAIVE' or 'LIST'
Returns:
an iterable of integers representing the schedule.
"""
if scheduler_alg == 'NAIV... | python | def minimize_peak_memory(graph, scheduler_alg):
"""Computes a schedule to minimize peak memory.
Args:
graph: an mtf.auto_mtf.graph_interface.GraphInterface.
scheduler_alg: a string, one of 'NAIVE' or 'LIST'
Returns:
an iterable of integers representing the schedule.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/scheduler.py | _minimize_peak_memory_list | def _minimize_peak_memory_list(graph):
"""Computes schedule according to the greedy list heuristic.
Greedy list heuristic: schedule the operation which results in the most bytes
of memory being (immediately) freed.
TODO(joshuawang): Experiment with tiebreaking by preferring more successors.
Args:
graph:... | python | def _minimize_peak_memory_list(graph):
"""Computes schedule according to the greedy list heuristic.
Greedy list heuristic: schedule the operation which results in the most bytes
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TODO(joshuawang): Experiment with tiebreaking by preferring more successors.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout.py | layout | def layout(mtf_graph, mesh_shape, mtf_outputs=()):
"""Compute layout rules based on a computational graph and mesh shape.
Args:
mtf_graph: a mtf.Graph.
mesh_shape: an mtf.Shape, str, or listlike of mtf.Dimension.
mtf_outputs: an optional iterable of mtf.Tensor, representing the outputs
of the c... | python | def layout(mtf_graph, mesh_shape, mtf_outputs=()):
"""Compute layout rules based on a computational graph and mesh shape.
Args:
mtf_graph: a mtf.Graph.
mesh_shape: an mtf.Shape, str, or listlike of mtf.Dimension.
mtf_outputs: an optional iterable of mtf.Tensor, representing the outputs
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tensorflow/mesh | mesh_tensorflow/optimize.py | Optimizer.apply_grads | def apply_grads(self, grads, variables):
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see mtf.rewrite_stack_variables().
Args:
grads: a list of Tensor
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tensorflow/mesh | mesh_tensorflow/auto_mtf/valid_layouts.py | LayoutValidator.is_valid_assignment | def is_valid_assignment(self, mtf_dimension_name, mesh_dimension_name):
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mtf_dimension_name: string, the name of a Mesh TensorFlow dimension.
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A b... | python | def is_valid_assignment(self, mtf_dimension_name, mesh_dimension_name):
"""Whether this MTF dimension may be assigned to this mesh dimension.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/valid_layouts.py | LayoutValidator._initialize_splittable_dimensions | def _initialize_splittable_dimensions(self, mtf_graph):
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Args:
mtf_graph: an mtf.Graph.
Returns:
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all_mtf_dimension_names = set() ... | python | def _initialize_splittable_dimensions(self, mtf_graph):
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mtf_graph: an mtf.Graph.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/valid_layouts.py | LayoutValidator._initialize_mtf_dimension_name_to_size_gcd | def _initialize_mtf_dimension_name_to_size_gcd(self, mtf_graph):
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tensorflow/mesh | mesh_tensorflow/auto_mtf/valid_layouts.py | LayoutValidator._initialize_mesh_dimension_name_to_size | def _initialize_mesh_dimension_name_to_size(self, mesh_shape):
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mesh_dimension_name_to_size = {} # {string: int}
for mesh_... | python | def _initialize_mesh_dimension_name_to_size(self, mesh_shape):
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tensorflow/mesh | mesh_tensorflow/placement_mesh_impl.py | allconcat_ring | def allconcat_ring(xs, devices, concat_axis):
"""Concatenate all Tensors everywhere.
Performance-optimized for a ring of devices.
Args:
xs: a list of n tf.Tensors
devices: a list of n strings
concat_axis: an integer
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xs: a list of n tf.Tensors
devices: a list of n strings
concat_axis: an integer
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tensorflow/mesh | mesh_tensorflow/placement_mesh_impl.py | PlacementMeshImpl.Print | def Print(self, x, data, message, **kwargs): # pylint: disable=invalid-name
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x: a LaidOutTensor
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tensorflow/mesh | mesh_tensorflow/placement_mesh_impl.py | PlacementMeshImpl.alltoall | def alltoall(self, x, mesh_axis, split_axis, concat_axis):
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x: a LaidOutTensor
mesh_axis: an integer the mesh axis along which to group
split_axis: an integer (the Tensor axis along which to split)
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x: a LaidOutTensor
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tensorflow/mesh | mesh_tensorflow/placement_mesh_impl.py | PlacementMeshImpl.import_tf_tensor | def import_tf_tensor(self, x, tf_x):
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Args:
x: a Tensor
tf_x: a tf.Tensor
Returns:
a LaidOutTensor
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | attention | def attention(q,
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | attention_params_simple | def attention_params_simple(
mesh, io_dim, kv_dim, heads_dim, variable_dtype):
"""Common case attention parameters.
Args:
mesh: a Mesh
io_dim: a Dimension (channels dimension of inputs and outputs)
kv_dim: a Dimension (channels in keys and values)
heads_dim: a Dimension (number of attention "he... | python | def attention_params_simple(
mesh, io_dim, kv_dim, heads_dim, variable_dtype):
"""Common case attention parameters.
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mesh: a Mesh
io_dim: a Dimension (channels dimension of inputs and outputs)
kv_dim: a Dimension (channels in keys and values)
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | local_attention_1d | def local_attention_1d(q,
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radius=128,
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | AttentionParams.compute_q | def compute_q(self, query_antecedent):
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Args:
query_antecedent: a Tensor with dimensions
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Returns:
a Tensor with dimensions
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"""
ret = mtf.einsum(
[query_antecedent... | python | def compute_q(self, query_antecedent):
"""Compute query Tensor q.
Args:
query_antecedent: a Tensor with dimensions
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Returns:
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | AttentionParams.compute_k | def compute_k(self, memory_antecedent):
"""Compute key Tensor k.
Args:
memory_antecedent: a Tensor with dimensions
{memory_input_dim} + other_dims
Returns:
a Tensor with dimensions
memory_heads_dims + {key_dim} + other_dims
"""
if self.shared_kv:
raise ValueError("... | python | def compute_k(self, memory_antecedent):
"""Compute key Tensor k.
Args:
memory_antecedent: a Tensor with dimensions
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Returns:
a Tensor with dimensions
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | AttentionParams.compute_v | def compute_v(self, memory_antecedent):
"""Compute value Tensor v.
Args:
memory_antecedent: a Tensor with dimensions
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Returns:
a Tensor with dimensions
memory_heads_dims + {value_dim} + other_dims
"""
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"""Compute value Tensor v.
Args:
memory_antecedent: a Tensor with dimensions
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a Tensor with dimensions
memory_heads_dims + {value_dim} + other_dims
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tensorflow/mesh | mesh_tensorflow/transformer/attention.py | AttentionParams.compute_output | def compute_output(self, o, output_shape=None):
"""Compute output of multihead attention.
Args:
o: a Tensor with dimensions
query_heads_dims + {value_dim} + other_dims
output_shape: an optional Shape
Returns:
a Tensor with shape:
{output_dim} + other_dims
"""
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"""Compute output of multihead attention.
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o: a Tensor with dimensions
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output_shape: an optional Shape
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tensorflow/mesh | mesh_tensorflow/transformer/t2t_vocabulary.py | T2tVocabulary.encode_tf | def encode_tf(self, s):
"""Encode a tf.Scalar string to a tf.Tensor.
This will be necessary for on-the-fly tokenization.
Args:
s: a tf.Scalar with dtype tf.string
Returns:
a 1d tf.Tensor with dtype tf.int32
"""
ids = subword_text_encoder_ops.subword_text_encoder_encode(
s, ... | python | def encode_tf(self, s):
"""Encode a tf.Scalar string to a tf.Tensor.
This will be necessary for on-the-fly tokenization.
Args:
s: a tf.Scalar with dtype tf.string
Returns:
a 1d tf.Tensor with dtype tf.int32
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tensorflow/mesh | mesh_tensorflow/transformer/model_builder.py | simple_layer_stack | def simple_layer_stack(include_encdec_attention,
num_layers=6,
d_ff=2048,
num_heads=8,
d_kv=128,
dropout_rate=0.1):
"""Create a layer stack.
Args:
include_encdec_attention: a boolean
num_layer... | python | def simple_layer_stack(include_encdec_attention,
num_layers=6,
d_ff=2048,
num_heads=8,
d_kv=128,
dropout_rate=0.1):
"""Create a layer stack.
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tensorflow/mesh | examples/toy_model_tpu.py | toy_model | def toy_model(features, mesh):
"""A toy model implemented by mesh tensorlfow."""
batch_dim = mtf.Dimension('batch', FLAGS.batch_size)
io_dim = mtf.Dimension('io', FLAGS.io_size)
master_dtype = tf.as_dtype(FLAGS.master_dtype)
slice_dtype = tf.as_dtype(FLAGS.slice_dtype)
activation_dtype = tf.as_dtype(FLAGS.... | python | def toy_model(features, mesh):
"""A toy model implemented by mesh tensorlfow."""
batch_dim = mtf.Dimension('batch', FLAGS.batch_size)
io_dim = mtf.Dimension('io', FLAGS.io_size)
master_dtype = tf.as_dtype(FLAGS.master_dtype)
slice_dtype = tf.as_dtype(FLAGS.slice_dtype)
activation_dtype = tf.as_dtype(FLAGS.... | [
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tensorflow/mesh | examples/toy_model_tpu.py | run_toy_model_tpu | def run_toy_model_tpu():
"""Run a toy model on TPU."""
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
iterations_per_loop = FLAGS.iterations
mesh_shape = mtf.convert_to_shape(FLAGS.mesh_shape)
config = tpu_config.RunConf... | python | def run_toy_model_tpu():
"""Run a toy model on TPU."""
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
iterations_per_loop = FLAGS.iterations
mesh_shape = mtf.convert_to_shape(FLAGS.mesh_shape)
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tensorflow/mesh | examples/mnist.py | mnist_model | def mnist_model(image, labels, mesh):
"""The model.
Args:
image: tf.Tensor with shape [batch, 28*28]
labels: a tf.Tensor with shape [batch] and dtype tf.int32
mesh: a mtf.Mesh
Returns:
logits: a mtf.Tensor with shape [batch, 10]
loss: a mtf.Tensor with shape []
"""
batch_dim = mtf.Dimens... | python | def mnist_model(image, labels, mesh):
"""The model.
Args:
image: tf.Tensor with shape [batch, 28*28]
labels: a tf.Tensor with shape [batch] and dtype tf.int32
mesh: a mtf.Mesh
Returns:
logits: a mtf.Tensor with shape [batch, 10]
loss: a mtf.Tensor with shape []
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tensorflow/mesh | examples/mnist.py | run_mnist | def run_mnist():
"""Run MNIST training and eval loop."""
mnist_classifier = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=FLAGS.model_dir)
# Set up training and evaluation input functions.
def train_input_fn():
"""Prepare data for training."""
# When choosing shuffle buffer sizes, l... | python | def run_mnist():
"""Run MNIST training and eval loop."""
mnist_classifier = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=FLAGS.model_dir)
# Set up training and evaluation input functions.
def train_input_fn():
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tensorflow/mesh | mesh_tensorflow/transformer/moe.py | MoE2D.call | def call(self, context, x, losses=None):
"""Call the layer."""
has_length_dim = context.length_dim in x.shape.dims
if not has_length_dim:
x_shape = x.shape
shape_with_length = mtf.Shape(
x_shape.dims[:-1] + [mtf.Dimension("length", 1)]
+ x_shape.dims[-1:])
x = mtf.resha... | python | def call(self, context, x, losses=None):
"""Call the layer."""
has_length_dim = context.length_dim in x.shape.dims
if not has_length_dim:
x_shape = x.shape
shape_with_length = mtf.Shape(
x_shape.dims[:-1] + [mtf.Dimension("length", 1)]
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tensorflow/mesh | mesh_tensorflow/auto_mtf/print_cp_model_solution.py | print_solution | def print_solution(model, solver):
"""Prints the solution associated with solver.
If solver has already had Solve() called on it, prints the solution. This
includes each variable and its assignment, along with the objective function
and its optimal value.
If solver has not had Solve() called on it, or there ... | python | def print_solution(model, solver):
"""Prints the solution associated with solver.
If solver has already had Solve() called on it, prints the solution. This
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | _local_var_name | def _local_var_name(splittable_dimensions, assignment):
"""Name for a local variable.
Args:
splittable_dimensions: frozenset of names of splittable dimensions.
assignment: dict from names of splittable dimensions to names of mesh
dimensions.
Returns:
A string, the variable name.
"""
assign... | python | def _local_var_name(splittable_dimensions, assignment):
"""Name for a local variable.
Args:
splittable_dimensions: frozenset of names of splittable dimensions.
assignment: dict from names of splittable dimensions to names of mesh
dimensions.
Returns:
A string, the variable name.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | _generate_assignments | def _generate_assignments(splittable_dimensions, mesh_dimension_to_size):
"""Generates all ways to map splittable dimensions to mesh dimensions.
Args:
splittable_dimensions: a frozenset of the names of splittable dimensions.
mesh_dimension_to_size: a dictionary from mesh dimension name to size.
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"""Generates all ways to map splittable dimensions to mesh dimensions.
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splittable_dimensions: a frozenset of the names of splittable dimensions.
mesh_dimension_to_size: a dictionary from mesh dimension name to size.
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer._preprocess_input | def _preprocess_input(self):
"""Computing useful input data structures to ease IP construction."""
# Compute the sets of MTF dimensions used in operations/tensors.
# a {string: frozenset(string)}, mapping operation name to MTF dimension
# names.
self._operation_name_to_mtf_dimension_set = {}
# ... | python | def _preprocess_input(self):
"""Computing useful input data structures to ease IP construction."""
# Compute the sets of MTF dimensions used in operations/tensors.
# a {string: frozenset(string)}, mapping operation name to MTF dimension
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer._initialize_variables | def _initialize_variables(self):
"""Initializing the variables of the IP."""
# Initialize global variables.
self._global_vars = {} # Indexed by (MTF dimension, mesh dimension)
for mtf_dimension_name in (
self._layout_validator.splittable_mtf_dimension_names):
for mesh_dimension_name in (
... | python | def _initialize_variables(self):
"""Initializing the variables of the IP."""
# Initialize global variables.
self._global_vars = {} # Indexed by (MTF dimension, mesh dimension)
for mtf_dimension_name in (
self._layout_validator.splittable_mtf_dimension_names):
for mesh_dimension_name in (
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer._add_constraints | def _add_constraints(self):
"""Adding constraints to the IP."""
# Add operation constraints.
for mesh_dimension_name in (
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sum(self._global_vars... | python | def _add_constraints(self):
"""Adding constraints to the IP."""
# Add operation constraints.
for mesh_dimension_name in (
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer._get_memory_contents | def _get_memory_contents(self):
"""Runs the scheduler to determine memory contents at every point in time.
Returns:
a list of frozenset of strings, where the ith entry describes the tensors
in memory when executing operation i (where schedule[i] is an index into
GetAllOperationNames()).
"... | python | def _get_memory_contents(self):
"""Runs the scheduler to determine memory contents at every point in time.
Returns:
a list of frozenset of strings, where the ith entry describes the tensors
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer.solve | def solve(self, print_solution=False):
"""Solves the current integer program and returns the computed layout.
Args:
print_solution: An optional boolean indicating whether to print the full
solution in human-readable format.
Returns:
The computed layout (as a string).
Raises:
... | python | def solve(self, print_solution=False):
"""Solves the current integer program and returns the computed layout.
Args:
print_solution: An optional boolean indicating whether to print the full
solution in human-readable format.
Returns:
The computed layout (as a string).
Raises:
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tensorflow/mesh | mesh_tensorflow/auto_mtf/layout_optimizer.py | LayoutOptimizer.evaluate_layout | def evaluate_layout(self, layout):
"""The current objective value for the given layout.
TODO(joshuawang): The current function does not check that the given
layout is valid.
Args:
layout: a string, representing a layout to evaluate (e.g.
"d_ff:m1;heads:m2").
Returns:
A float... | python | def evaluate_layout(self, layout):
"""The current objective value for the given layout.
TODO(joshuawang): The current function does not check that the given
layout is valid.
Args:
layout: a string, representing a layout to evaluate (e.g.
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Returns:
A float... | [
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tensorflow/mesh | mesh_tensorflow/utils.py | BalancedVariablePlacer.device_function | def device_function(self, var):
"""Choose a device for the input variable.
Args:
var: an Variable.
Returns:
The device for placing the var.
"""
if var.type not in ('Variable', 'VariableV2', 'VarHandleOp'):
tf.logging.debug('Place {} on last device: {}.'.format(
var.name... | python | def device_function(self, var):
"""Choose a device for the input variable.
Args:
var: an Variable.
Returns:
The device for placing the var.
"""
if var.type not in ('Variable', 'VariableV2', 'VarHandleOp'):
tf.logging.debug('Place {} on last device: {}.'.format(
var.name... | [
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tensorflow/mesh | mesh_tensorflow/beam_search.py | greedy_decode | def greedy_decode(logits_fn,
initial_ids,
temperature=0.0,
initial_states=None,
eos_id=EOS_ID,
forced_ids=None,
use_tpu=True):
"""Greedy decoding.
Args:
logits_fn: Interface to the model, to provide logi... | python | def greedy_decode(logits_fn,
initial_ids,
temperature=0.0,
initial_states=None,
eos_id=EOS_ID,
forced_ids=None,
use_tpu=True):
"""Greedy decoding.
Args:
logits_fn: Interface to the model, to provide logi... | [
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] | 3921196e5e43302e820da0a87329f25d7e2a3016 | https://github.com/tensorflow/mesh/blob/3921196e5e43302e820da0a87329f25d7e2a3016/mesh_tensorflow/beam_search.py#L577-L642 | train |
tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | pack_and_batch | def pack_and_batch(dataset, batch_size, length, pack=True):
"""Create a tf.data.Dataset which emits training batches.
The input dataset emits feature-dictionaries where each feature is a vector
of integers ending in EOS=1
The tensors in the returned tf.data.Dataset have shape
[batch_size, length]. Zeros in... | python | def pack_and_batch(dataset, batch_size, length, pack=True):
"""Create a tf.data.Dataset which emits training batches.
The input dataset emits feature-dictionaries where each feature is a vector
of integers ending in EOS=1
The tensors in the returned tf.data.Dataset have shape
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | encode_dataset | def encode_dataset(dataset, vocabulary):
"""Encode from strings to token ids.
Args:
dataset: a tf.data.Dataset with string values.
vocabulary: a mesh_tensorflow.transformer.Vocabulary
Returns:
a tf.data.Dataset with integer-vector values ending in EOS=1
"""
def encode(features):
return {k: vo... | python | def encode_dataset(dataset, vocabulary):
"""Encode from strings to token ids.
Args:
dataset: a tf.data.Dataset with string values.
vocabulary: a mesh_tensorflow.transformer.Vocabulary
Returns:
a tf.data.Dataset with integer-vector values ending in EOS=1
"""
def encode(features):
return {k: vo... | [
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | packed_parallel_tsv_dataset | def packed_parallel_tsv_dataset(filenames=gin.REQUIRED,
dataset_split=gin.REQUIRED,
batch_size=gin.REQUIRED,
sequence_length=gin.REQUIRED,
vocabulary=gin.REQUIRED,
... | python | def packed_parallel_tsv_dataset(filenames=gin.REQUIRED,
dataset_split=gin.REQUIRED,
batch_size=gin.REQUIRED,
sequence_length=gin.REQUIRED,
vocabulary=gin.REQUIRED,
... | [
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | supervised_to_dict | def supervised_to_dict(dataset, text2self):
"""Turns a supervised dataset into a dataset with a feature dictionary.
if text2self, then the features dictionary contains a "targets" key.
else, the features dictionary contains "inputs" and "targets" keys.
Args:
dataset: a tf.data.Dataset
text2self: a boo... | python | def supervised_to_dict(dataset, text2self):
"""Turns a supervised dataset into a dataset with a feature dictionary.
if text2self, then the features dictionary contains a "targets" key.
else, the features dictionary contains "inputs" and "targets" keys.
Args:
dataset: a tf.data.Dataset
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | encode_all_features | def encode_all_features(dataset, vocabulary):
"""Encode all features.
Args:
dataset: a tf.data.Dataset
vocabulary: a vocabulary.Vocabulary
Returns:
a tf.data.Dataset
"""
def my_fn(features):
ret = {}
for k, v in features.items():
v = vocabulary.encode_tf(v)
v = tf.concat([tf.t... | python | def encode_all_features(dataset, vocabulary):
"""Encode all features.
Args:
dataset: a tf.data.Dataset
vocabulary: a vocabulary.Vocabulary
Returns:
a tf.data.Dataset
"""
def my_fn(features):
ret = {}
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tensorflow/mesh | mesh_tensorflow/transformer/dataset.py | pretokenized_tfrecord_dataset | def pretokenized_tfrecord_dataset(filenames,
text2self,
eos_included,
repeat,
batch_size,
sequence_length):
"""Reads tensor2tensor-style data files.... | python | def pretokenized_tfrecord_dataset(filenames,
text2self,
eos_included,
repeat,
batch_size,
sequence_length):
"""Reads tensor2tensor-style data files.... | [
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